fix: 自动创建KPI时补全所有NOT NULL字段(formula/data_source/data_owner/unit/entity_id)

backend/app/api/analysis_results.py

@@ -0,0 +1,184 @@
+"""置信度评分系统 — 财务Bot分析结论管理"""
+from fastapi import APIRouter, Depends, HTTPException, Query
+from sqlalchemy.orm import Session
+from sqlalchemy import func
+from app.database import get_db
+from app.auth_middleware import require_auth
+from app.models import AnalysisResult, KPIValue, KPIDefinition
+from typing import Optional, List
+from datetime import datetime
+
+router = APIRouter(prefix="/api/cma/analysis", tags=["置信度评分"],
+    dependencies=[Depends(require_auth)],
+)
+
+
+def _calc_confidence(has_actual: bool, has_target: bool, has_trend: bool, has_review: bool) -> int:
+    """基于数据完整度自动计算置信度"""
+    if has_review:
+        return 95
+    if has_actual and has_target and has_trend:
+        return 85
+    if has_actual and has_target:
+        return 70
+    if has_actual:
+        return 50
+    return 30  # 无实际值，基于推测
+
+
+@router.get("/result")
+async def get_analysis_results(
+    period: Optional[str] = Query(None, description="期间 YYYY-MM"),
+    kpi_code: Optional[str] = Query(None, description="KPI编码"),
+    db: Session = Depends(get_db),
+):
+    """查询分析结论（按期间和/或KPI编码过滤）"""
+    query = db.query(AnalysisResult).order_by(AnalysisResult.created_at.desc())
+
+    if period:
+        query = query.filter(AnalysisResult.period == period)
+    if kpi_code:
+        query = query.filter(AnalysisResult.kpi_code == kpi_code)
+
+    results = query.all()
+
+    return {
+        "total": len(results),
+        "period": period,
+        "results": [
+            {
+                "id": r.id,
+                "period": r.period,
+                "结论": r.conclusion,
+                "置信度": f"{r.confidence}%",
+                "数据来源": r.data_source,
+                "计算逻辑": r.calculation_logic,
+                "可比基准": r.comparable_benchmark,
+                "局限": r.limitations,
+                "kpi_code": r.kpi_code,
+                "kpi_name": r.kpi_name,
+                "has_actual": bool(r.has_actual),
+                "has_target": bool(r.has_target),
+                "has_trend": bool(r.has_trend),
+                "has_review": bool(r.has_review),
+                "created_at": r.created_at.strftime("%Y-%m-%d %H:%M:%S") if r.created_at else None,
+            }
+            for r in results
+        ],
+    }
+
+
+@router.post("/result")
+async def create_analysis_result(
+    period: str = Query(..., description="期间 YYYY-MM"),
+    conclusion: str = Query(..., description="分析结论"),
+    data_source: Optional[str] = Query(None, description="数据来源"),
+    calculation_logic: Optional[str] = Query(None, description="计算逻辑"),
+    comparable_benchmark: Optional[str] = Query(None, description="可比基准"),
+    limitations: Optional[str] = Query(None, description="局限说明"),
+    kpi_code: Optional[str] = Query(None, description="关联KPI编码"),
+    kpi_name: Optional[str] = Query(None, description="关联KPI名称"),
+    has_actual: bool = Query(False, description="有实际值"),
+    has_target: bool = Query(False, description="有目标值"),
+    has_trend: bool = Query(False, description="有历史趋势"),
+    has_review: bool = Query(False, description="有人工复核"),
+    db: Session = Depends(get_db),
+):
+    """提交分析结果（自动计算置信度）"""
+    confidence = _calc_confidence(has_actual, has_target, has_trend, has_review)
+
+    result = AnalysisResult(
+        period=period,
+        conclusion=conclusion,
+        confidence=confidence,
+        data_source=data_source,
+        calculation_logic=calculation_logic,
+        comparable_benchmark=comparable_benchmark,
+        limitations=limitations,
+        kpi_code=kpi_code,
+        kpi_name=kpi_name,
+        has_actual=1 if has_actual else 0,
+        has_target=1 if has_target else 0,
+        has_trend=1 if has_trend else 0,
+        has_review=1 if has_review else 0,
+    )
+    db.add(result)
+    db.commit()
+    db.refresh(result)
+
+    return {
+        "id": result.id,
+        "period": result.period,
+        "结论": result.conclusion,
+        "置信度": f"{result.confidence}%",
+        "数据来源": result.data_source,
+        "计算逻辑": result.calculation_logic,
+        "可比基准": result.comparable_benchmark,
+        "局限": result.limitations,
+        "confidence_score": result.confidence,
+        "message": "分析结论已保存",
+    }
+
+
+@router.delete("/result/{result_id}")
+async def delete_analysis_result(
+    result_id: int,
+    db: Session = Depends(get_db),
+):
+    """删除分析结论"""
+    result = db.query(AnalysisResult).filter(AnalysisResult.id == result_id).first()
+    if not result:
+        raise HTTPException(404, "分析结论不存在")
+    db.delete(result)
+    db.commit()
+    return {"message": "已删除"}
+
+
+@router.post("/auto-calculate")
+async def auto_calculate_confidence(
+    period: str = Query(..., description="期间 YYYY-MM"),
+    kpi_code: str = Query(..., description="KPI编码"),
+    db: Session = Depends(get_db),
+):
+    """根据KPI数据完整性自动生成置信度评分"""
+    # 查找KPI定义
+    kpi = db.query(KPIDefinition).filter(KPIDefinition.kpi_code == kpi_code).first()
+    if not kpi:
+        raise HTTPException(404, f"KPI编码 {kpi_code} 不存在")
+
+    # 查找该期间的实际值
+    value = db.query(KPIValue).filter(
+        KPIValue.kpi_id == kpi.id,
+        KPIValue.period == period,
+    ).first()
+
+    # 查找历史数据（趋势）
+    trend_values = db.query(KPIValue).filter(
+        KPIValue.kpi_id == kpi.id,
+    ).order_by(KPIValue.period.desc()).limit(6).all()
+
+    has_actual = value is not None and value.actual_value is not None
+    has_target = kpi.target_value is not None
+    has_trend = len(trend_values) >= 2
+    has_review = False
+
+    confidence = _calc_confidence(has_actual, has_target, has_trend, has_review)
+
+    return {
+        "kpi_code": kpi_code,
+        "kpi_name": kpi.kpi_name,
+        "period": period,
+        "has_actual": has_actual,
+        "has_target": has_target,
+        "has_trend": has_trend,
+        "has_review": has_review,
+        "confidence": confidence,
+        "confidence_label": f"{confidence}%",
+        "数据完备度": {
+            "10%": "无数据",
+            "50%": "有实际值",
+            "70%": "有实际值+目标值",
+            "85%": "有实际值+目标值+历史趋势",
+            "95%": "有全部数据+人工复核",
+        }.get(str(confidence), "基于推测"),
+    }

backend/app/api/data.py

@@ -244,11 +244,17 @@ async def import_excel_smart(file: UploadFile = File(...), db: Session = Depends
         if not kpi_code:
             new_code = f"{stype_prefix}{len(kpis) + created_kpis + 1:03d}"
             new_kpi = KPIDefinition(
+                entity_id=1,
                 kpi_code=new_code,
                 kpi_name=clean_name,
                 dimension="finance",
                 category="financial_report",
+                formula="-",
                 data_source_type="excel",
+                data_source="Excel导入",
+                data_owner="财务部",
+                frequency="monthly",
+                unit="元",
                 status="active",
             )
             db.add(new_kpi)

backend/app/main.py

@@ -5,7 +5,7 @@ from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse
 from dotenv import load_dotenv
 from app.database import init_db
-from app.api import auth, kpis, templates, maps, dashboard, data, alerts, ai_analysis, alert_rules, users, thresholds, notifications, permissions, action_plans, alignment, org, objectives, versions, budget, cost, predict, reports, security, knowledge, bot_bridge, bot_bridge_v2, lead, customer_dashboard, deviation_push, budget_generate, knowledge_articles, kpi_causality, data_quality, bi_reports, entities, bsc_layers, okr, okr_templates, subjects, driver_budget, bot_kpis, bot_iron_law
+from app.api import auth, kpis, templates, maps, dashboard, data, alerts, ai_analysis, alert_rules, users, thresholds, notifications, permissions, action_plans, alignment, org, objectives, versions, budget, cost, predict, reports, security, knowledge, bot_bridge, bot_bridge_v2, lead, customer_dashboard, deviation_push, budget_generate, knowledge_articles, kpi_causality, data_quality, bi_reports, entities, bsc_layers, okr, okr_templates, subjects, driver_budget, bot_kpis, bot_iron_law, analysis_results
 from app.utils.cache import clear_all as clear_cache, delete as delete_cache
 from scripts.erp_sync import run_sync as run_erp_sync
 from app.auth_middleware import require_auth
@@ -72,6 +72,7 @@ app.include_router(subjects.router)
 app.include_router(driver_budget.router)
 app.include_router(bot_kpis.router)
 app.include_router(bot_iron_law.router)
+app.include_router(analysis_results.router)
 
 @app.exception_handler(Exception)
 async def global_exception_handler(request: Request, exc: Exception):

backend/app/models/__init__.py

@@ -435,6 +435,26 @@ class BudgetDeviationAlert(Base):
     created_at = Column(DateTime, server_default=func.now())
 
 
+class AnalysisResult(Base):
+    """财务Bot分析结论 — 带置信度评分"""
+    __tablename__ = "analysis_results"
+    id = Column(Integer, primary_key=True, index=True)
+    period = Column(String(20), nullable=False, comment="期间 YYYY-MM")
+    conclusion = Column(String(1000), nullable=False, comment="分析结论")
+    confidence = Column(Integer, nullable=False, comment="置信度 0-100")
+    data_source = Column(String(500), nullable=True, comment="数据来源")
+    calculation_logic = Column(String(1000), nullable=True, comment="计算逻辑")
+    comparable_benchmark = Column(String(500), nullable=True, comment="可比基准")
+    limitations = Column(String(1000), nullable=True, comment="局限说明")
+    has_actual = Column(Integer, default=0, comment="有实际值")
+    has_target = Column(Integer, default=0, comment="有目标值")
+    has_trend = Column(Integer, default=0, comment="有历史趋势")
+    has_review = Column(Integer, default=0, comment="有人工复核")
+    kpi_code = Column(String(50), nullable=True, comment="关联KPI编码")
+    kpi_name = Column(String(200), nullable=True, comment="关联KPI名称")
+    created_at = Column(DateTime, server_default=func.now())
+
+
 class ForecastAccuracy(Base):
     """预测准确率 — 上期预测 vs 本期实际"""
     __tablename__ = "forecast_accuracy"